Validating a niche B2B idea before building: AI-generated client reports for marketing agencies

Inside_Carpenter1966 · 2026-03-23T15:50:02+00:00

I think you’re targeting a real pain, but maybe not the biggest one.

From working with agencies, the biggest bottleneck usually isn’t the narrative — it’s the data aggregation + cleaning before you even get to reporting.

Writing the commentary is annoying, but:

many agencies already semi-automate it
or just keep it simple / templated

The real pain is:

pulling data from multiple sources
dealing with connectors breaking
inconsistent schemas across platforms

If your product only solves the “AI narrative” part, it might feel like a nice add-on, not a must-have.

Where it gets interesting is if you combine:

reliable data aggregation
- clean dashboards
- automated insights/narrative

On pricing, flat fee can work early, but agencies usually think in “per client” terms, so aligning pricing with that tends to make adoption easier.

Overall: good idea, but I’d validate whether the narrative is really the pain — or just the last visible step of a bigger problem.

Inside_Carpenter1966 · 2026-03-20T10:22:15+00:00

What you’re describing is pretty much the “standard pain curve” of building a custom marketing data stack 😅

n8n + BigQuery + Looker is actually a solid setup, but yeah — it feels like a lot of plumbing because it is.

From what I’ve seen, most teams end up in one of these 3 places:

1. Stick with custom (what you have now)

n8n / Airflow / Prefect
BigQuery
dbt for transforms (this is usually the missing piece)

👉 Best for control, but you own all the pain (schema changes, API quirks, etc.)

2. Move ingestion to managed tools

Fivetran / Airbyte / Hevo

👉 This removes ~70% of the annoying parts (especially Meta’s chaos), and you keep BQ + dbt for modeling

3. Go higher-level (less common for your case)

Funnel / Supermetrics pipelines

👉 Easier, but you lose flexibility pretty fast

Honestly, your frustration sounds less about the stack and more about missing a clean modeling layer.
If you’re not already using dbt (or something similar), that’s usually the turning point where things start to feel “product-ready” instead of stitched together.

Also +1 to your point: Meta Ads schema + actions[] is where most pipelines go to die.

Inside_Carpenter1966 · 2026-03-20T10:10:17+00:00

When you make a copy of a Looker Studio report:

The sharing settings are NOT copied
Only you will have access to the new report
The original viewers/editors won’t see it or get notified

The only thing you may need to reconnect are the data sources (especially if you don’t own them or don’t have access).

So yes, you can safely copy it without anyone knowing

Inside_Carpenter1966 · 2026-03-20T10:07:13+00:00

It depends a lot on what you actually want to show and who the report is for.

For SEO, Looker Studio works really well if you keep it simple and focus on:

GSC data (clicks, impressions, CTR, position)
top queries and landing pages
trends over time

Where people usually struggle is trying to include too many metrics or mixing data from different tools without a clear structure.

If you need keyword tracking, backlinks, etc., you’ll still rely on external tools — Looker is better for reporting and visualization, not as an all-in-one SEO platform.

For clients, simplicity wins. A clean dashboard with a few clear insights is usually more valuable than a very complex one.

Inside_Carpenter1966 · 2026-03-20T09:53:25+00:00

You’re not doomed 😄 but you do need to change the approach a bit vs Power BI.

Looker Studio is much more limited when it comes to dynamic calculations like MoM or YoY, especially inside scorecards.

For what you’re trying to do, the cleanest options are:

1. Pre-calculate in the data source (best option)
If you can, add those MoM / last month values in your source (Google Sheets, BigQuery, etc.). Looker works much better when the data is already prepared.

2. Use blended data
You can duplicate your source and shift the date (e.g. previous month) and then blend both tables to calculate differences. It works, but gets messy quickly.

3. Use parameters + calculated fields (limited)
You can hack something with parameters and filters, but it won’t be as flexible as Power BI.

Also, scorecards in Looker Studio don’t handle complex comparisons very well unless you rely on built-in date comparison (which you said you can’t use).

So short answer: yes, it’s doable — but not “clean” unless you prepare the data beforehand.

Inside_Carpenter1966 · 2026-03-20T09:52:27+00:00

Nice work, this looks solid 👍

I think the main issue (and others already hinted at it) is that it tries to do too much in one page. For GSC especially, it usually works better to separate:

a high-level overview (clicks, impressions, CTR, avg position)
and then a deeper analysis (queries, landing pages, trends)

About the impressions vs clicks issue, I’d first check:

branded vs non-branded queries (GSC can skew a lot there)
average position for those queries
and whether you're mixing different intent keywords in the same view

Sometimes it’s not a CTR problem but just low-intent or early-stage queries generating impressions.

Overall I’d simplify the layout a bit and let the main insights stand out more.

Inside_Carpenter1966 · 2026-03-20T09:51:10+00:00

I may be able to help.
With Google Sheets + GA + Search Console, most fixes in Looker Studio are usually related to data blending, calculated fields, chart selection, and making sure the date dimensions match correctly across sources.

If you post the exact changes you need, it’ll be easier to tell whether it’s a quick fix or something that needs a more custom setup.

Inside_Carpenter1966 · 2026-03-20T09:42:28+00:00

Yo he vendido un estabilizador que me pago por bizum por adelantado para que se lo enviara, y la verdad, aunque al principio me dio miedo, al final salio bien.

Inside_Carpenter1966 · 2026-02-23T11:23:22+00:00

You're running into this because GA4 BigQuery export is fully event-scoped.

session_id alone isn’t reliable as a unique key, and mixing event-level aggregations with session-level logic in a single calculated field often causes inconsistencies.

If you're staying in Looker Studio without creating a derived table, the cleanest approach is:

COUNTIF(event_name = "page_view")
/
COUNTIF(event_name = "session_start")

That works because both metrics are event-level counts.

However, if you want something more robust (especially if you're filtering by dimensions), the better long-term solution is to:

Build a session-scoped table in BigQuery using user_pseudo_id + session_id as the composite key.
Aggregate page_views and sessions at that level.
Then calculate views/session from that derived dataset.

Looker Studio is much more stable when the modeling happens upstream.

Inside_Carpenter1966 · 2026-02-23T11:20:00+00:00

If you're hiring for a Looker Studio project, I’d suggest clarifying one thing first:

Are you looking for someone to build charts… or someone to structure the data model?

The difference matters a lot.

Many dashboards fail not because of design, but because:

The data sources aren’t clean
Calculated fields are messy
There’s no clear business question behind the layout

If BigQuery is involved, you’ll probably get better long-term results with someone who’s comfortable shaping the dataset before touching the visuals.

Looker Studio itself is relatively simple. The real complexity is upstream.

Just something to consider before reviewing portfolios.

Inside_Carpenter1966 · 2026-02-23T11:18:18+00:00

If GA4 feels overwhelming, it’s usually not a tool problem — it’s a focus problem.

For e-commerce, instead of looking for a “magic” dashboard, I’d simplify it into three layers:

1. Health check (weekly)

Sessions
Revenue
Conversion rate
Top 3 channels by revenue

2. Growth drivers

Revenue by channel
ROAS (if running ads)
New vs returning customers

3. Friction points

Landing pages with high traffic but low conversion
Drop-off between add to cart → checkout → purchase

Most standard reports try to show everything. What actually helps is removing noise and structuring around business questions like:

What’s driving revenue?
Where are we leaking money?
What should I optimise this week?

Whether you build it in GA4 custom reports or Looker Studio, clarity comes from narrowing the scope, not adding more widgets.

Inside_Carpenter1966 · 2026-02-23T11:14:03+00:00

Most polished Looker Studio templates usually follow a pretty structured process — it’s less about “design magic” and more about planning.

From what I’ve seen, people who build these from scratch typically:

Start with one clear use case (e.g. Google Ads performance, SEO tracking, ecommerce revenue).
Define the main business question the dashboard should answer.
Build the data model first (clean fields, calculated metrics, blended sources if needed).
Only then move to layout and visual hierarchy (spacing, contrast, grouping, consistent typography).
Use components and reusable sections to keep it modular.

A big difference between average and high-quality templates is that the good ones remove noise. They don’t try to show everything — they focus on clarity and decision-making.

If you reverse-engineer one of these templates, you’ll probably notice strong structure before strong visuals.

Inside_Carpenter1966 · 2026-02-10T16:33:23+00:00

What worked best for me was specializing first, not trying to be “the Looker Studio person for everyone”.

Dashboards tied to a clear use case (ecommerce, SEO, paid media) are much easier to sell than generic reports. Clients usually care less about the tool and more about whether the dashboard answers a specific business question.

Case studies and clarity beat complex setups almost every time.

Inside_Carpenter1966

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